Although the above papers have made great progress on the power dispatch of distribution network with high-penetration renewable energy, there are still significant gaps to fill. References [1]}, [2]}, [3]}, [4]}, [5]} utilized the demand-side sources to avoid the additional investment, but the power flow security constraints are ignored. References [6]}, [7]}, [8]}, [9]}, [10]} built the security constraints via the power flow equations, but they require accurate distribution network model that may be unknown in many practical cases. Recently, deep reinforcement learning was employed to optimize the power dispatch [11]}. However, unlike the white-box models which can be solved by off-the-shelf solvers efficiently, those learning-based models are time-consuming and can not guarantee global optimality.
